Predictive model for glycemic control in patients with diabetes mellitus

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  • Fatma Hilal Yagin Biostatistics and Medical Informatics, Faculty of Medicine, Inonu University, Malatya, Türkiye
  • Badicu Georgian Department of Physical Education and Special Motricity, Transilvania University of Brasov, 500068 Brasov, Romania



Diabetes mellitus, glycemic control, machine learning, predictive modeling


Diabetes mellitus, a chronic disease characterized by high blood sugar levels, necessitates effective glycemic control to prevent severe complications such as damage to the heart, blood vessels, eyes, kidneys, and nerves. This study aims to utilize machine learning techniques to predict glycemic control among a open Access dataset of 77,723 newly diagnosed diabetic patients in Istanbul. By employing a logistic regression model, the study identifies key features influencing glycemic control, enhancing model interpretability for clinicians. The model demonstrates robust performance with an accuracy of 0.825, precision scores of 0.86 (positive class) and 0.76 (negative class), recall values of 0.86 (positive class) and 0.77 (negative class), and corresponding F1 scores. Feature importance analysis reveals HbA1c as the dominant predictor, significantly surpassing other variables. These findings provide critical insights into the application of machine learning in diabetes management, highlighting the pivotal role of HbA1c in glycemic control prediction.




How to Cite

Yagin , F. H., & Georgian, B. (2024). Predictive model for glycemic control in patients with diabetes mellitus. Journal of Exercise Science & Physical Activity Reviews, 2(1), 91–96.



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